Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

We study the resilience to Byzantine failures of distributed implementations of Stochastic
Gradient Descent (SGD). So far, distributed machine learning frameworks have largely …

While machine learning is going through an era of celebrated success, concerns have been
raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent …

While machine learning is going through an era of celebrated success, concerns have been
raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent …

Asynchronous distributed machine learning solutions have proven very effective so far, but
always assuming perfectly functioning workers. In practice, some of the workers can …

Deep learning (DL) reliability is becoming a growing concern, and efficient reliability
assessment approaches are required to meet safety constraints. This article presents a …

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in
many scenarios, including safety-critical applications such as autonomous driving. In this …

The latest generation of IoT systems incorporate machine learning (ML) technologies on
edge devices. This introduces new engineering challenges to bring ML onto resource …

Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as
autonomous vehicles, demands a reliable and efficient execution on hardware. The design …